GBMM1D

MATLAB通信仿真pdg教程MATLAB Tutorial Communication Simulation pdg(MATLAB Simulation of Communication pdg Tutorial MATLAB Tutorial Communication Simulation pdg)

Guide for DS1307 Real Time Clock

二级倒立摆的仿真 用simulink对二级倒立摆的模糊控制仿真(Simulation of double inverted pendulum with a double inverted pendulum on simulink fuzzy control simulation)

说明：  用Matlab产生正弦波,矩形波,以及白噪声信号，并显示各自时域波形图 进行FFT变换，显示各自频谱图，其中采样率，频率、数据长度自选 做出上述三种信号的均方根图谱,功率图谱,以及对数均方根图谱 用IFFT傅立叶反变换恢复信号，并显示恢复的正弦信号时域波形图(fft analysis,plot rms spectrum,power density spectrum, etc.)

说明：  鲸鱼优化算法的源程序，注释很详细，并且已经调试成功，很好用(Whale Optimization Algorithm Source Code, the comments are very detailed)

matlab programmer plc

denoise In BayesShrink[5] we determine the threshold for each subband assuming a Generalized Gaussian Distribution(GGD) . The GGD is given by GG¾ X ¯ (x) = C(¾ X ¯ )exp¡ [® (¾ X ¯ )jxj]¯ (6) ¡ 1 < x < 1 ¯ > 0, where ® (¾ X ¯ ) = ¾ ¡ 1 X [ ¡ (3=¯ ) ¡ (1=¯ ) ]1=2 and C(¾ X ¯ ) = ¯ ¢ ® (¾ X ¯ ) 2¡ ( 1 ¯ ) and ¡ (t) = R1 0 e¡ uut¡ 1du. The parameter ¾ X is the standard deviation and ¯ is the shape parameter It has been observed[5] that with a shape parameter ¯ ranging from 0.5 to 1, we can describe the the distribution of coefficients in a subband for a large set of natural images.Assuming such a distribution for the wavelet coefficients, we empirically estimate ¯ and ¾ X for each subband and try to find the threshold T which minimizes the Bayesian Risk, i.e, the expected value of the mean square error.

SIMULINK是MATLAB软件的扩展，它是实现动态系统建模和仿真的一个软件包，它与MATLAB语言的主要区别在于，其与用户交互接口是基于Windows的模型化图形输入，其结果是使得用户可以把更多的精力投入到系统模型的构建，而非语言的编程上。 (SIMULINK is an extension of MATLAB software, which is the dynamic system modeling and simulation of a software package with the main difference is that the MATLAB language and its interaction with the user interface is Windows-based model of graphical input, the result is to make users put more energy into the system model of the building, rather than the programming language.)

Filter Gauss Low pass

Another particle filter implementation (by by Diego Andrés Alvarez Marín) that implements Arulampalam et. al. (2002). A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing. 50 (2). p 174--188